CN108009391A - A kind of multiple dimensioned lower Grouped point object similarity calculating method - Google Patents
A kind of multiple dimensioned lower Grouped point object similarity calculating method Download PDFInfo
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- CN108009391A CN108009391A CN201710394763.2A CN201710394763A CN108009391A CN 108009391 A CN108009391 A CN 108009391A CN 201710394763 A CN201710394763 A CN 201710394763A CN 108009391 A CN108009391 A CN 108009391A
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Abstract
In order to study the similitude of Grouped point object under different scale, using adaptive space clustering method, Grouped point object is clustered, obtains " blank zone domain inside Grouped point object.By the research to white space, corresponding similarity calculation method is proposed；The factor of influence of Grouped point object similitude, including topological relation, direction relations, distribution, distance relation etc. are found, and proposes corresponding similarity calculating method, finally integrates integral point group similarity calculation.The present invention can accurately express the difference of Grouped point object similitude between different scale, and the retrieval of the quality evaluation and Grouped point object to cartographic generaliztion has certain reference value.
Description
Technical field
The invention belongs to cartography and Geographical Information Sciences technical field, proposes a kind of multiple dimensioned Grouped point object similarity meter
Calculation method, it is intended to evaluated for Map Generalization quality.
Background technology
Multiple dimensioned map space figure similarity relation is the hot issue of GIS researchs.The similarity relation of extraterrestrial target includes
The similarity relation of simple target and the similarity relation of space group target, the similarity relation of simple target and group's target it is similar
The research of relation, inquiry and analysis for spatial data, the tissue of spatial data and reasoning, the evaluation tool of cartographic generaliztion quality
It is significant.
The model of description Grouped point object similarity relation was more simple in the past, and such as Krzysztof and Janowicz are inquired into
The problem of Semantic Similarity of spatial simlanty；Schwering analyze geometrical model, feature model, network model etc. for
Geographic object and its possibility of concept similarity are described；The distribution of point group is not said using convex hull principle in river sea
It is bright.In addition, in terms of the holding for being related to spatial relationship, domestic and foreign scholars have done many researchs, such as Li and Fonseca
TDD (topology-direction-distance) spatial simlanty descriptive model；Fourth rainbow has carried out spatial simlanty theory
Research, establish the meter of similarity for spatial directions, space topological similitude, Spatial Semantics similitude and spatial scene similitude
Calculate model；Yan Haowen, Chu Yandong have inquired into taxonomic hierarchies of multiple dimensioned map space similarity relation etc..
A part of the white space as Grouped point object in Grouped point object, research for Grouped point object similitude and point
Analysis is of great significance.The analysis of white space extracts the sky of Grouped point object for studying the regularity of distribution of space group target
Between architectural feature, predict Grouped point object development tendency have the function that it is important.Also, the extraction of white space is for solution
The geographical phenomenon for releasing complexity has great importance.
The content of the invention
It is an object of the invention to propose a kind of method for calculating multiple dimensioned Grouped point object similarity relation, there is provided space phase
Like application of the relation in terms of Map Generalization quality is evaluated, the inner link between multiple dimensioned Grouped point object is objectively reflected, is protected
The cognition that evaluation result meets the mankind is demonstrate,proved.
The method of the present invention includes proposing Grouped point object white space similarity and Grouped point object overall similarity.
The similarity relation of multiple dimensioned Grouped point object is the important component of spatial relationship.Its calculation procedure is as follows, utilizes
Adaptive space clustering method, extracts the white space in Grouped point object；The phase of multiple dimensioned Grouped point object is proposed on this basis
Like the spatial relationship similarity between degree computational methods, including the geometric similarity degree and extraterrestrial target of extraterrestrial target（Topological similarity,
Direction similarity and Distance conformability degree）；And Grouped point object white space similarity and Grouped point object overall similarity are proposed respectively
Computation model.By the analysis of geometric properties, spatial relationship to multiple dimensioned Grouped point object white space and Grouped point object entirety,
Obtain accurate overall similarity computation model.
The present invention is intended to provide a kind of computational methods of multiple dimensioned Grouped point object similarity, can accurately carry out charting comprehensive
The evaluation of conjunction, as a result meets the cognition custom of people.
Brief description of the drawings
Fig. 1 is flow chart
Grouped point object figure under Fig. 2 different scales
Fig. 3 is point group cluster and " peeling " result figure afterwards
Fig. 4 is the direction Voronoi diagram of white space
Fig. 5 is point group " the MABR figures in blank zone domain
Fig. 6 is the Voronoi diagram of point group
Embodiment
For technology contents, construction feature, the purpose realized and the effect reached that the present invention will be described in detail, below
Described in detail with reference to embodiment.
The implementation steps of the present invention may be summarized to be two parts：The geometric similarity of white space in multiple dimensioned Grouped point object
Degree, spatial relationship similarity measure and overall Grouped point object geometric properties and spatial relationship similarity measure.Below to each implementation
Step is further elaborated.
The technical solution adopted in the present invention is to follow the steps below：
1. the geometric similarity degree of white space, spatial relationship similarity measure in multiple dimensioned Grouped point object：
Step 1：The white space in Grouped point object is extracted using adaptive space clustering method
Step 2:It is proposed in Grouped point object " direction relations in blank zone domain, and represented with direction Voronoi diagram model:
SIMBDir
Step 3：In Grouped point object " distance relation in blank zone domain, and represented with Hausdorff distances：
Step 4：It is proposed " the geometric properties calculating formula of similarity in blank zone domain：
SIMBShp=
Wherein, Shp=, Shp1 and Shp2 represent the geometrical characteristic of Grouped point object before and after change of scale.
Step 5：It is proposed to calculate white space overall similarity computational methods：
SIMBlank=
2. multiple dimensioned Grouped point object similarity：
Step 1：The method for proposing to calculate the topological relation similarity between extraterrestrial target using topological relation field figure：
Step 2：Utilize " blank zone domain central point and the line of Grouped point object central point and the company in the minimum area rectangle lower left corner
The angle that line is formed, proposes direction relations similarity calculating method：
Step 3：Using the distance between target centroid, distance relation similarity calculating method is proposed：
SIMDis
Step 4：After " peeling " operates, the side of borderline Delaunay triangulation network, which combines, just obtains Grouped point object
The area of periphery, removes the area of white space, has just obtained band " distribution of blank zone domain Grouped point object, distribution
Similarity calculating method is：
Step 5：It is proposed the computational methods of Grouped point object overall similarity：
SIMEntity=
Step 6：It is proposed the overall model of the multiple dimensioned Grouped point object similarity of calculating：
SIM=
In conclusion the present invention provides the geometric similarity degree of white space, spatial relationship similarity in multiple dimensioned Grouped point object
Computational methods, meet the spatial cognition of people, can objectively reflect the inner link between multiple dimensioned Grouped point object, comprehensive to charting
Closing result has good evaluation effect.
Claims (1)
1. propose geometric similarity degree, the spatial relationship similarity calculating method of white space in multiple dimensioned Grouped point object：
Step 1：The white space in Grouped point object is extracted using adaptive space clustering method；
Step 2：It is proposed in Grouped point object " direction relations in blank zone domain, and represented with direction Voronoi diagram model；
Step 3：It is proposed in Grouped point object " distance relation in blank zone domain, and represented with Hausdorff distances；
Step 4：It is proposed " the geometric properties calculating formula of similarity in blank zone domain；
Step 5：It is proposed to calculate white space overall similarity computational methods；
It is proposed multiple dimensioned Grouped point object overall similarity computational methods：
Step 1：The method for proposing to calculate the topological relation similarity between extraterrestrial target using topological relation field figure；
Step 2：Utilize " blank zone domain central point and the line of Grouped point object central point and the company in the minimum area rectangle lower left corner
The angle that line is formed, proposes direction relations similarity calculating method；
Step 3：Using the distance between target centroid, distance relation similarity calculating method is proposed；
Step 4：After " peeling " operates, the side of borderline Delaunay triangulation network, which combines, just obtains Grouped point object
The area of periphery, removes the area of white space, represents the band " distribution of blank zone domain Grouped point object；
Step 5：It is proposed the computational methods of Grouped point object overall similarity；
Step 6：It is proposed the overall model of the multiple dimensioned Grouped point object similarity of calculating；
In conclusion the present invention extracts white space in Grouped point object using adaptive space clustering method, and with white space
For constraints, the similarity calculating method of multiple dimensioned Grouped point object is proposed, it was demonstrated that the method disclosure satisfy that Map Generalization quality
Evaluation.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108615059A (en) * | 2018-05-10 | 2018-10-02 | 中国人民解放军战略支援部队信息工程大学 | A kind of lake automatically selecting method and device based on Dynamic Multiscale cluster |
| CN116795347A (en) * | 2023-06-30 | 2023-09-22 | 兰州交通大学 | Map-oriented automatic comprehensive graphical programming system and device |
| CN117251740A (en) * | 2023-09-22 | 2023-12-19 | 兰州交通大学 | Multi-feature-considered point group similarity evaluation method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103278810A (en) * | 2013-05-10 | 2013-09-04 | 南京航空航天大学 | Method for extracting dimension characteristics of underwater target based on space similarity |
| CN103326904A (en) * | 2013-07-12 | 2013-09-25 | 中国人民解放军理工大学 | Rapid network topology inferring method based on multi-parameter cognition |
| US20130346844A1 (en) * | 2012-06-22 | 2013-12-26 | Microsoft Corporation | Checking and/or completion for data grids |
| CN104317793A (en) * | 2014-08-27 | 2015-01-28 | 河南理工大学 | Different-period spatial entity hierarchical matching method and system based on multi-source information |
-
2017
- 2017-05-29 CN CN201710394763.2A patent/CN108009391A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130346844A1 (en) * | 2012-06-22 | 2013-12-26 | Microsoft Corporation | Checking and/or completion for data grids |
| CN103278810A (en) * | 2013-05-10 | 2013-09-04 | 南京航空航天大学 | Method for extracting dimension characteristics of underwater target based on space similarity |
| CN103326904A (en) * | 2013-07-12 | 2013-09-25 | 中国人民解放军理工大学 | Rapid network topology inferring method based on multi-parameter cognition |
| CN104317793A (en) * | 2014-08-27 | 2015-01-28 | 河南理工大学 | Different-period spatial entity hierarchical matching method and system based on multi-source information |
Non-Patent Citations (6)
| Title |
|---|
| YONGYANG XU 等，: "Shape similarity measurement model for holed polygons based on position graphs and Fourier descriptors", 《INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE》 * |
| 刘涛 等，: "空间面群目标几何相似度计算模型", 《地球信息科学学报》 * |
| 段晓旗 等，: "基于层次分析法的多尺度点群目标相似度计算", 《地球信息科学》 * |
| 段晓旗 等，: "空白区域对点群相似性特征因子的影响", 《测绘科学》 * |
| 王中辉，: "环境地理空间群组目标方向关系计算模型研究", 《中国博士学位论文全文数据库基础科学辑》 * |
| 王骁 等，: "顾及邻域居民地群组相似性的道路网匹配方法", 《测绘学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108615059A (en) * | 2018-05-10 | 2018-10-02 | 中国人民解放军战略支援部队信息工程大学 | A kind of lake automatically selecting method and device based on Dynamic Multiscale cluster |
| CN108615059B (en) * | 2018-05-10 | 2020-08-07 | 中国人民解放军战略支援部队信息工程大学 | Lake automatic selection method and device based on dynamic multi-scale clustering |
| CN116795347A (en) * | 2023-06-30 | 2023-09-22 | 兰州交通大学 | Map-oriented automatic comprehensive graphical programming system and device |
| CN117251740A (en) * | 2023-09-22 | 2023-12-19 | 兰州交通大学 | Multi-feature-considered point group similarity evaluation method |
| CN117251740B (en) * | 2023-09-22 | 2024-04-19 | 兰州交通大学 | Multi-feature-considered point group similarity evaluation method |
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